Abstract: Disclosed are a virtual power plant operation risk analysis method and a device. The method comprises: establishing a multi-state model of wind turbine output, analyzing influence of wind speed on wind turbine failure rate based on the multi-state model of wind turbine output, and establishing a wind turbine failure model considering wind turbine time-varying failure rate; establishing a multi-state model of wind turbine output considering the wind speed and the wind turbine time-varying failure rate by an improved general generating function method based on the multi-state model of wind turbine output and the wind turbine failure model considering the wind turbine time-varying failure rate; establishing a multi-state output model of virtual power plant based on the multi-state model of wind turbine output considering wind speed and wind turbine time-varying failure rate; and calculating operation risk indicators of virtual power plant through the multi-state output model of virtual power plant.

Abstract: Disclosed is an electric vehicle load aggregation method based on continuous tracking of wind power curve, including: constructing an electric vehicle load consumption wind power curve aggregation model to obtain an electric vehicle call result, and calculating the abandoned wind power through the electric vehicle call result; optimizing the abandoned wind power quantity by energy storage equipment to obtain the abandoned wind power quantity after energy storage adjustment and optimization, setting the energy storage power and capacity configuration, and constructing a wind power curve continuous tracking model after energy storage adjustment and optimization; and solving the charging and discharging power of the energy storage equipment in each time period based on the wind power curve continuous tracking model after the energy storage adjustment and optimization, and calculating the cost of output aggregation.

Abstract: The present invention relates to a reliability calculation method of a power distribution system considering hierarchical decentralized control of demand-side resources, including the following steps: step 1, establishing a multi-state model of wind turbine output and a two-state model of wind turbine failure, which respectively consider randomness of wind speed and uncertainty of wind turbine failure; step 2, establishing a multi-state reliability model of distributed wind power system including a plurality of wind turbines; step 3, establishing a reliability model of information communication system considering hierarchical decentralized control of random failures and information delays; step 4, establishing a reliability model of demand-side resources considering hierarchical decentralized control; and step 5, calculating a value of reliability of a power distribution system considering hierarchical decentralized control of demand-side loads, to acquire an analysis result of the reliability of the power di

Abstract: Disclosed is a prediction method and device for a clearing price of an auxiliary service for peak regulation, including: acquiring continuous N days of historical data of clearing prices of an auxiliary service market for peak regulation and respectively moving data of original clearing prices forwards by 1, 2, . . . k days, so as to obtain D?1, D?2 . . . D?k data columns; performing first-round training and prediction by adopting a BP (Back Propagation) neural network, a BP neural network optimized by a PSO (Particle Swarm Optimization) algorithm and an LSSVM (Least Square Support Vector Machine), and forming a BP-expansion column, a PSO_BP-expansion column and an LSSVM-expansion column; training the BP neural network by taking first N?k days of time points and D?1, D?2 . . . D?k clearing prices, the BP-expansion column, the PSO_BP-expansion column, the LSSVM-expansion column and the original clearing prices as training data; and performing prediction for the clearing price.

Abstract: The present invention discloses a wind power consumption method of a virtual power plant with consideration of comprehensive demand responses of electrical loads and heat loads, which comprises: establishing a wind turbine output model, so as to obtain a wind power prediction curve; establishing heat load demand models before/after demand responses and heat supply equipment output models before/after the demand responses, so as to obtain the abandoned wind quantities per moment before/after the demand responses and the total abandoned wind quantities before/after the demand responses; judging that whether consumption is promoted or not according to the total abandoned wind quantities before/after the demand responses; and establishing a storage battery capacity model and judging the charging/discharging state and the charging/discharging capacity of a storage battery.

Abstract: The invention relates to an optimal allocation method for stored energy coordinating electric vehicles (EVs) to participate in auxiliary service market (ASM), including the following steps: 1. Predict the reported capacity of daily 96 points for EVs to participate in the ASM by least square support vector machine (LSSVM). 2. Fit the daily total load distribution of EVs. 3. Determine the error distribution between the reported capacity and the actual response capacity, and simulate the total daily load capacity of EVs in the future with Monte Carlo method. 4. Calculate the energy storage capacity required by EVs daily participating in ASM. 5. Build the objective function to minimize the scheduling risk of auxiliary service. 6. Solve the energy storage model in step 5 with particle swarm optimization (PSO), and output the configuration results of optimal energy storage capacity and energy storage power. The invention can improve the adjustable capacity of EVs participating in ASM.

Abstract: The present disclosure relates to a method for clustering forecasting of the electric vehicle charging load, comprising the following steps: collecting electric vehicle charging load data on a historical date and weather information data related to that historical date; preprocessing and then normalizing the collected data to obtain a new data set; performing fuzzy C-means clustering on the normalized data, and taking an actual load measurement point as a fuzzy clustering index to construct a similar daily load set of the date to be forecast; according to the similar daily load set, constructing and training a least-square SVM (support vector machine) forecasting model; inputting load values at the same time in three days ahead of the date to be forecast and the weather information data related to the three days into the trained least-square SVM forecasting model, and outputting a forecast load.

Abstract: The present invention relates to a load forecasting method based on a multi-energy coupling scene, which comprises the following steps: step 1, establishing a multilevel indicator system of key influencing factors of load demand in a multi-energy mode; step 2, obtaining key influencing factors influencing the total load demand and the total supply of the multi-energy coupling load; step 3, normalizing the data of the key influencing factors extracted in step 2, and initializing population characteristic parameters of adaptive firework algorithm (AFWA); step 4, forecasting the regional total power demand and regional coupling power supply respectively by adopting LSSVM optimized by the AFWA algorithm; and step 5, forecasting the net power load demand on the regional coupling power supply. The present invention improves the calculation efficiency and model stability and also ensures the forecasting accuracy.

Abstract: The invention relates to a multi-subject flexible energy block bidding transaction system and method in a power market. The system includes a login authentication module, a data input module, a first processing module, a second processing module, a third processing module, a data verifying module and an output module, wherein the login authentication module is configured for self-identity confirmation of a power market subject and selecting a power transaction declaration mode; the data input module is configured for inputting an electricity price, an electricity quantity and corresponding time information; the three processing modules carry out matching under different modes, the data verifying module receives matching results obtained by each processing module and carries out safety verification, and the output module outputs final trading results of an electricity quantity and an electricity price of each subject in the power market.

Abstract: The invention discloses a genetically engineered bacterium in which the gene encoding adenine deaminase on the genome of the bacterium is knocked out or/and the gene encoding the enzyme in the NAD+ anabolic pathway is integrated on the genome of the bacterium. The invention also discloses a construction method of the above-mentioned genetically engineered bacteria. The gene encoding adenine deaminase on the genome of the host strain is knocked out to obtain a strain with high NAD+ yield. Or the expression cassettes of the gene encoding the enzyme in the NAD+ synthesis pathway are constructed separately, and then the enzyme encoding The gene expression cassette is integrated into the genome of the host strain whose gene encoding adenine deaminase is knocked out to construct a strain with high NAD+ production. The application of the above genetically engineered bacteria is disclosed. A method of producing NAD+ is disclosed.

Abstract: The present invention relates to a load forecasting method based on a multi-energy coupling scene, which comprises the following steps: step 1, establishing a multilevel indicator system of key influencing factors of load demand in a multi-energy mode; step 2, obtaining key influencing factors influencing the total load demand and the total supply of the multi-energy coupling load; step 3, normalizing the data of the key influencing factors extracted in step 2, and initializing population characteristic parameters of adaptive firework algorithm (AFWA); step 4, forecasting the regional total power demand and regional coupling power supply respectively by adopting LSSVM optimized by the AFWA algorithm; and step 5, forecasting the net power load demand on the regional coupling power supply. The present invention improves the calculation efficiency and model stability and also ensures the forecasting accuracy.

Abstract: An automatic reliability analysis method for a warm standby system based on a multi-state decision diagram is provided. A multi-state model is constructed for the system with multi-state components; then, according to possible state transitions of every component in the system, the multi-state decision diagram is constructed. Through construction, simplification and decomposition of the multi-state decision diagram, an occurrence probability of each path in the multi-state decision diagram is obtained with integral operation. The occurrence probability of each path in the multi-state decision diagram is further calculated with considering a failure probability of activation of the warm standby components, so as to obtain system reliability. The present invention takes the warm standby system with the multi-state components as an object, and is able to programmatically process the multi-state components whose state transitions follow arbitrary distributions with high accuracy and fast computing speed.